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Abstract Non‐destructive detection of wire bonding defects in integrated circuits (IC) is critical for ensuring product quality after packaging. Image‐processing‐based methods do not provide a detailed evaluation of the three‐dimensional defects of the bonding wire. Therefore, a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision, which can achieve non‐destructive detection of bonding wire defects is proposed. The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires. Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface, a point cloud segmentation method based on spatial surface feature detection (SFD) was proposed. SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process. Furthermore, in the defect detection process, a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires. The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires. The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires, and the average accuracy of defect recognition is 96.47%, which meets the production requirements of bonding wire defect detection.

Abstract Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production lines. During the wafer testing stage, deep learning methods are widely used in wafer defect detection due to their powerful feature extraction capabilities. However, most of the current wafer defect patterns classification models have high complexity and slow detection speed, which are difficult to apply in the actual wafer production process. In addition, there is a data imbalance in the wafer dataset that seriously affects the training results of the model. To reduce the complexity of the deep model without affecting the wafer feature expression, this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM‐PeleeNet based on the PeleeNet module. In addition, to reduce the impact of data imbalance on model training, this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden layer. The method proposed in this paper has an average accuracy of 95.4% on the WM‐811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs, and takes only 22.99 s to detect 1000 wafer pictures. Compared with the original PeleeNet network without optimization, the number of parameters and FLOPs are reduced by 92.68% and 58.85%, respectively. Data augmentation on the minority class wafer map improves the average classification accuracy by 1.8% on the WM‐811K dataset. At the same time, the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved.

Abstract Physiological studies have shown that rats in a dark environment rely on the limbs and vestibule for their self‐motion information, which can maintain the specific firing patterns of grid cells and hippocampal CA3 place cells. In the development stage of rats, grid cells are considered to come from place cells, and place cells can be encoded by hippocampal theta cells. Based on these, the quadruped robot is used as a platform in this paper. Firstly, the sensing information of the robot's limbs and inertial measurement unit is obtained to solve its position in the environment. Then the position information is encoded by theta cells and mapped to place cells through a neural network. After obtaining the place cells with single‐peak firing fields, Hebb learning is used to adjust the connection weight of the neural network between place cells and grid cells. In order to verify the model, 3‐D simulation experiments are designed in this paper. The experiment results show that with the robot exploring in space, the spatial cells firing effects obtained by the model are consistent with the physiological research facts, which lay the foundation for the bionic environmental cognition model.

Introduction: Ellis-van Creveld (EVC) syndrome is an uncommon genetic disease that can be diagnosed at any age. Observation: A case of EVC syndrome was reported in a young 3-year-old female patient presenting chondroectodermal dysplasia, polydactyly, congenital heart defects, damage to the oral mucosa and numerous dental alterations (number, form and structure). Oral management consists of teaching oral hygiene and the prophylactic filling of dental cracks. Discussion: EVC is an autosomal recessive disease. Its diagnosis is only based on clinical features and genetic studies. Conclusion: Dentists should be aware of this syndrome to avoid a late diagnosis and to facilitate a multidisciplinary management.

Abstract Background In sub-Saharan Africa, the availability and accessibility of oral health services are seriously constrained and the provision of essential oral care is limited. Reports from the region show a very low utilization of oral health care services, and visits to dental-care facilities are mostly undertaken for symptomatic reasons. The objectives of the present study were to describe the prevalence of oral symptoms among adults in Ouagadougou, capital city of Burkina Faso and the use of oral health services and self-medication in response to these symptoms and to measure the associations between predisposing, enabling and needs factors and decisions to seek oral health care. Methods The conceptual design of the study was derived from both the Andersen-Newman model of health care utilization and the conceptual framework of the WHO International Collaborative Study of Oral Health Outcomes. Data were obtained by two-stage stratified sampling through four areas representative of different stages of urbanization of Ouagadougou. The final study population comprised 3030 adults aged 15 years or over and the response rate was 65%. Results Overall, 28% of the respondents had experienced an oral health problem during the past 12 months; a high proportion (62%) reported pain or acute discomfort affecting daily life. In response to symptoms, only 28% used oral health facilities, 48% used self-medication and 24% sought no treatment at all. Multivariate analyses revealed that several socio-economic and socio-cultural factors such as religious affiliation, material living conditions and participation in a social network were significantly associated with the use of oral health care services by adults who had experienced oral health problems during the previous year. Conclusion The proportion of people who have obtained oral health care is alarmingly low in Ouagadougou and self-medication appears to be an important alternative source of care for adult city-dwellers. Decision-makers in sub-Saharan countries must seek to ensure that access to essential oral health care is improved.

Some age and growth aspects of Engraulis encrasicolus (Pisces, Engraulidae) along the coast of Ivory Coast. Age and growth of Engraulis encrasicolus (Linneaus, 1758) were studied in eastern coast of Ivory coast from September 2004 to September 2005. Fishes were caught monthly using seines of beach and purse seines sliding. A sample of 1400 fishes were caught for studies. Standard-length (SL) was measured to the nearest mm and body weight (W) to the nearest 0.01 g. Groups age was established by frequency distribution from mode progression. Individual age was estimated from scales study method. The scales of anchovies are cycloid with very small size. May was choosen as new born month. In September, at a putative age of 5 months, E. encrasicolus presented a standard-length of 5.5±1.02 cm. Three age classes were obtained in relation of rings presence. The length-weight relationship indicates that length increases faster than weight (b= 2.5953